Clinicians and GPs will soon be able to better identify patients who are at a higher risk of serious illness from SARS-CoV-2 infection based on a new data-driven risk prediction model, now under development by a number of UK universities and NHS Digital.
This new model could be applied in a variety of health and care settings, including supporting GPs and specialists in consultations with their patients to provide more targeted advice based on individual levels of risk.
The model could also be used to inform mathematical modelling of the potential effect of national public health policies on shielding and preventing infection and potentially help identify those at highest risk to be vaccinated, when available.
UK government guidance on COVID-19 identifies individuals based on three broad categories of risk, with those who are 'clinically extremely vulnerable' to the disease previously being advised to shield themselves from the virus.
The new model will use routinely collected anonymised electronic health records of eight million adults in the UK, accessed through the University of Oxford’s QResearch database and linked data sets, to identify factors that can be used to predict those at highest risk of infection and serious illness from COVID-19. These include age, sex, ethnicity, deprivation, smoking status, body mass index, pre-existing medical conditions and current medications.
Algorithms from the data analysis will be developed in conjunction with clinical and data experts at NHS Digital and will drive a clinical risk prediction model. Individualised risk assessment could be used to improve shared decision-making between clinicians and patients based on more accurate information.
This model project is a commission from the Office of the Chief Medical Officer for England to New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG).
